Solar photocatalytic membranes: an experimental and artificial neural network modeling approach for niflumic acid degradation

Detalhes bibliográficos
Autor(a) principal: Aoudjit, Lamine
Data de Publicação: 2022
Outros Autores: Salazar, Hugo, Zioui, Djamila, Sebti, Aicha, Martins, Pedro Manuel Abreu, Lanceros-Méndez, S.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/80751
Resumo: The presence of contaminants of emerging concern (CEC), such as pharmaceuticals, in water sources is one of the main concerns nowadays due to their hazardous properties causing severe effects on human health and ecosystem biodiversity. Niflumic acid (NFA) is a widely used anti-inflammatory drug, and it is known for its non-biodegradability and resistance to chemical and biological degradation processes. In this work, a 10 wt.% TiO<sub>2</sub>/PVDF–TrFE nanocomposite membrane (NCM) was prepared by the solvent casting technique, fully characterized, and implemented on an up-scaled photocatalytic membrane reactor (PMR). The photocatalytic activity of the NCM was evaluated on NFA degradation under different experimental conditions, including NFA concentration, pH of the media, irradiation time and intensity. The NCM demonstrated a remarkable photocatalytic efficiency on NFA degradation, as efficiency of 91% was achieved after 6 h under solar irradiation at neutral pH. The NCM proved effective in long-term use, with maximum efficiency losses of 7%. An artificial neural network (ANN) model was designed to model NFA’s photocatalytic degradation behavior, demonstrating a good agreement between experimental and predicted data, with an R<sup>2</sup> of 0.98. The relative significance of each experimental condition was evaluated, and the irradiation time proved to be the most significant parameter affecting the NFA degradation efficiency. The designed ANN model provides a reliable framework l for modeling the photocatalytic activity of TiO<sub>2</sub>/PVDF-TrFE and related NCM.
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spelling Solar photocatalytic membranes: an experimental and artificial neural network modeling approach for niflumic acid degradationartificial neural networkmodellingnanocomposite membraneniflumic acidphotocatalysisphotocatalytic membrane reactorScience & TechnologyThe presence of contaminants of emerging concern (CEC), such as pharmaceuticals, in water sources is one of the main concerns nowadays due to their hazardous properties causing severe effects on human health and ecosystem biodiversity. Niflumic acid (NFA) is a widely used anti-inflammatory drug, and it is known for its non-biodegradability and resistance to chemical and biological degradation processes. In this work, a 10 wt.% TiO<sub>2</sub>/PVDF–TrFE nanocomposite membrane (NCM) was prepared by the solvent casting technique, fully characterized, and implemented on an up-scaled photocatalytic membrane reactor (PMR). The photocatalytic activity of the NCM was evaluated on NFA degradation under different experimental conditions, including NFA concentration, pH of the media, irradiation time and intensity. The NCM demonstrated a remarkable photocatalytic efficiency on NFA degradation, as efficiency of 91% was achieved after 6 h under solar irradiation at neutral pH. The NCM proved effective in long-term use, with maximum efficiency losses of 7%. An artificial neural network (ANN) model was designed to model NFA’s photocatalytic degradation behavior, demonstrating a good agreement between experimental and predicted data, with an R<sup>2</sup> of 0.98. The relative significance of each experimental condition was evaluated, and the irradiation time proved to be the most significant parameter affecting the NFA degradation efficiency. The designed ANN model provides a reliable framework l for modeling the photocatalytic activity of TiO<sub>2</sub>/PVDF-TrFE and related NCM.This research was funded by Fundação para a Ciência e Tecnologia (FCT) grant numbers SFRH/BD/122373/2016 and COVID/BD/151786/2021 and contract 2020.02802.CEECIND.This work was supported by Solar Equipment Development Unit (UDES) and Portuguese Foundation for Science and Technology (FCT) in the framework of the Strategic Projects UID/FIS/04650/20132019 and UID/QUI/50006/2019 and project PTDC/FIS-MAC/28157/2017. H. Salazar and P. M. Martins thank the FCT for the grants SFRH/BD/122373/2016 and COVID/BD/151786/2021, and the contract 2020.02802.CEECIND. Financial support from the Basque Government Industry and Education Departments under the ELKARTEK program is also acknowledged.Multidisciplinary Digital Publishing InstituteUniversidade do MinhoAoudjit, LamineSalazar, HugoZioui, DjamilaSebti, AichaMartins, Pedro Manuel AbreuLanceros-Méndez, S.2022-08-302022-08-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/80751engAoudjit, L.; Salazar, H.; Zioui, D.; Sebti, A.; Martins, P.M.; Lanceros-Méndez, S. Solar Photocatalytic Membranes: An Experimental and Artificial Neural Network Modeling Approach for Niflumic Acid Degradation. Membranes 2022, 12, 849. https://doi.org/10.3390/membranes120908492077-037510.3390/membranes12090849https://www.mdpi.com/2077-0375/12/9/849info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:49:14Zoai:repositorium.sdum.uminho.pt:1822/80751Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:47:40.123278Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Solar photocatalytic membranes: an experimental and artificial neural network modeling approach for niflumic acid degradation
title Solar photocatalytic membranes: an experimental and artificial neural network modeling approach for niflumic acid degradation
spellingShingle Solar photocatalytic membranes: an experimental and artificial neural network modeling approach for niflumic acid degradation
Aoudjit, Lamine
artificial neural network
modelling
nanocomposite membrane
niflumic acid
photocatalysis
photocatalytic membrane reactor
Science & Technology
title_short Solar photocatalytic membranes: an experimental and artificial neural network modeling approach for niflumic acid degradation
title_full Solar photocatalytic membranes: an experimental and artificial neural network modeling approach for niflumic acid degradation
title_fullStr Solar photocatalytic membranes: an experimental and artificial neural network modeling approach for niflumic acid degradation
title_full_unstemmed Solar photocatalytic membranes: an experimental and artificial neural network modeling approach for niflumic acid degradation
title_sort Solar photocatalytic membranes: an experimental and artificial neural network modeling approach for niflumic acid degradation
author Aoudjit, Lamine
author_facet Aoudjit, Lamine
Salazar, Hugo
Zioui, Djamila
Sebti, Aicha
Martins, Pedro Manuel Abreu
Lanceros-Méndez, S.
author_role author
author2 Salazar, Hugo
Zioui, Djamila
Sebti, Aicha
Martins, Pedro Manuel Abreu
Lanceros-Méndez, S.
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Aoudjit, Lamine
Salazar, Hugo
Zioui, Djamila
Sebti, Aicha
Martins, Pedro Manuel Abreu
Lanceros-Méndez, S.
dc.subject.por.fl_str_mv artificial neural network
modelling
nanocomposite membrane
niflumic acid
photocatalysis
photocatalytic membrane reactor
Science & Technology
topic artificial neural network
modelling
nanocomposite membrane
niflumic acid
photocatalysis
photocatalytic membrane reactor
Science & Technology
description The presence of contaminants of emerging concern (CEC), such as pharmaceuticals, in water sources is one of the main concerns nowadays due to their hazardous properties causing severe effects on human health and ecosystem biodiversity. Niflumic acid (NFA) is a widely used anti-inflammatory drug, and it is known for its non-biodegradability and resistance to chemical and biological degradation processes. In this work, a 10 wt.% TiO<sub>2</sub>/PVDF–TrFE nanocomposite membrane (NCM) was prepared by the solvent casting technique, fully characterized, and implemented on an up-scaled photocatalytic membrane reactor (PMR). The photocatalytic activity of the NCM was evaluated on NFA degradation under different experimental conditions, including NFA concentration, pH of the media, irradiation time and intensity. The NCM demonstrated a remarkable photocatalytic efficiency on NFA degradation, as efficiency of 91% was achieved after 6 h under solar irradiation at neutral pH. The NCM proved effective in long-term use, with maximum efficiency losses of 7%. An artificial neural network (ANN) model was designed to model NFA’s photocatalytic degradation behavior, demonstrating a good agreement between experimental and predicted data, with an R<sup>2</sup> of 0.98. The relative significance of each experimental condition was evaluated, and the irradiation time proved to be the most significant parameter affecting the NFA degradation efficiency. The designed ANN model provides a reliable framework l for modeling the photocatalytic activity of TiO<sub>2</sub>/PVDF-TrFE and related NCM.
publishDate 2022
dc.date.none.fl_str_mv 2022-08-30
2022-08-30T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/80751
url https://hdl.handle.net/1822/80751
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Aoudjit, L.; Salazar, H.; Zioui, D.; Sebti, A.; Martins, P.M.; Lanceros-Méndez, S. Solar Photocatalytic Membranes: An Experimental and Artificial Neural Network Modeling Approach for Niflumic Acid Degradation. Membranes 2022, 12, 849. https://doi.org/10.3390/membranes12090849
2077-0375
10.3390/membranes12090849
https://www.mdpi.com/2077-0375/12/9/849
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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